Mastering Looker Studio Data Sources for Reporting

To create effective reports in Looker Studio, you need to understand how to work with data sources. Looker Studio supports a wide range of data sources, including Google Analytics, Google Sheets, and SQL databases.

Using the right data source is crucial for accurate reporting. For example, using Google Analytics as a data source allows you to track website interactions and behavior.

A data source can be connected to Looker Studio in just a few clicks. This makes it easy to start creating reports without needing to know complex data management skills.

Once a data source is connected, you can use Looker Studio's drag-and-drop interface to create visualizations and reports. This interface is user-friendly and requires minimal technical expertise.

To get started with Looker Studio, begin by exploring your data without building a complete report. This is great, so you don’t have a lot of unfinished dashboards on your account.

To use the explorer function, go to the Looker Studio home page and click on ‘Create’ and select the ‘Explorer’ option. This will give you access to a wide range of data sources.

You can select a data source you have used previously or add a new data source to the explorer. Looker Studio has sample data sets available, so you can play with them to get a feel for the tool.

To gain insights fast, add charts and graphs at the top left corner and add the metrics and dimensions to identify trends.

Defining data sources in Looker Studio is a crucial step in creating meaningful reports. To add a data source, you need to follow six steps.

You can find all the Looker Studio connectors by clicking on 'Add data' or 'Resource' > 'Manage added data sources' > 'Add data source'. The platform has integrations with almost any platform and database, such as Google BigQuery, Google Sheets, Google Analytics 4, Facebook Ads, and many more.

To add multiple data sources to your report, you need to follow the same process, just click 'add data', select the data source, and then follow the authentication process.

Here are the different form elements you can use to get user input in the configuration screen:

In this article, you'll learn the ins and outs of defining data sources. A Looker Studio Community Connector is a powerful tool that allows you to connect external data sources to your Looker Studio reports.

You'll discover how to use Google Apps Script to build a Community Connector, which is a crucial step in unlocking the full potential of your data.

By the end of this article, you'll know how to use Community Connectors in Looker Studio, making it easier to create custom reports and visualizations.

Here's a quick overview of what you can expect to learn:

You'll need to define the getConfig() function to configure your connector before users can start using it. This function response defines the configuration options users will see, and Looker Studio will render the connector configuration screen based on it.

The getConfig() function response should include a configParams object with configuration values for the parameters defined in getConfig() and configured by the user. It should also include a dateRangeRequired property set to true to provide date ranges with all data requests.

You can use the following form elements in the configuration screen: TEXTINPUT for a single-line text box, TEXTAREA for a multi-line textarea box, SELECT_SINGLE for a dropdown for single-select options, SELECT_MULTIPLE for a dropdown for multi-select options, CHECKBOX for a single checkbox that can be used to capture boolean values, and INFO for a static plain-text box to provide instructions or information to the user.

Here's an example of how you can use the INFO element to provide user instructions and a TEXTINPUT element to get the input package name from the user:

```html

{

"configParams": {

"package_name": {

"type": "TEXTINPUT",

"label": "Package Name"

},

"instructions": {

"type": "INFO",

"value": "Please enter the package name"

}

},

"dateRangeRequired": true

}

```

The configParams object should be grouped under the configParams key in the getConfig() response.

In the request object structure, the configParams object will contain configuration values for the parameters defined in getConfig() and configured by the user. The scriptParams object will contain information relevant to connector execution, but you don't need to use it for this codelab. The dateRange will contain the requested date range if requested in getConfig() response. The fields will contain the list of names of fields for which data is requested.

Here's an example request from the getData() function:

```json

{

"configParams": {

"package_name": "my_package"

},

"dateRange": {

"startDate": "2022-01-01",

"endDate": "2022-01-31"

},

"fields": ["field1", "field2"]

}

```

This request includes the configuration values for the package name and date range, and the list of fields for which data is requested.

You can access a wide variety of data sources through the more than 600 partner connectors that instantly enable you to connect virtually any kind of data, without any coding or software.

With Looker Studio, you can connect to data without limits, thanks to its extensive library of partner connectors.

You can connect virtually any kind of data, including Google BigQuery, Google Sheets, Google Analytics 4, Facebook Ads, and many more.

According to Example 8, you can access a wide variety of data sources through the more than 600 partner connectors.

Here are some examples of data sources you can connect to with Looker Studio:

These connectors enable you to connect virtually any kind of data, without any coding or software.

You'll want to be mindful of the records limit when working with data in Looker Studio. There's a limit of 150,000 rows.

If you try to load more data than that, your data might be incomplete or Looker Studio will slow down. You can check how much data you're sending by clicking on the three dots next to your source and selecting Data Preview.

To give you a better idea, here are some options to consider when you have more data:

Configuring Data Sources is a crucial step in setting up your Looker Studio report. You can add multiple data sources to your report and combine them to make calculated fields to measure KPIs such as the cost per acquisition and total ad spend.

To configure a data source, you need to follow the authentication process after selecting the data source. You must authenticate to Looker Studio and authorize your account with the platform you want to connect with. The authentication process is slightly different depending on the data sources you are connecting to.

You can add a new data source by clicking 'Add data', selecting the data source, and then following the authentication process. It's also recommended to give your data sources unique labels in Looker Studio.

The response from the npm API will be in a specific format that we need to transform and filter for our requested fields.

In the response, we'll return the schema for only the requested fields using the schema property. We'll also return the data using the rows property as a list of rows.

The sequence of fields in values must match the sequence of fields in schema, as demonstrated in our earlier example of request.

This means that for each row, the fields in values must be in the same order as the fields in schema.

The response for getData() will look like a list of rows, with each row containing the requested fields in the correct order.

Configuring the connector is a crucial step in setting up your data source in Looker Studio. Type in "lighthouse" in the text input area and click Connect in the top right.

To configure the data source, you'll need to select specific settings for your data source. This will help you tailor the data to your needs and ensure you're getting the insights you want.

Once you've authorized the connection, you'll see a prompt to configure the data source. Click on the settings icon to access the configuration options.

Here are some common configuration options you may need to set up:

Be sure to check the documentation for your specific data source for more information on configuration options.

After configuring the data source, you'll need to add it to your report. This will make the data available for creating visualizations.

To add a data source in Looker Studio, you need to sign in, create or select a report, add a data source, authorize the connection, configure the data source, and add it to the report. You can also add multiple data sources and combine them to create calculated fields.

You can connect any data source to Looker Studio in six steps: login, add data, select a connector, authorize the connection, add the data to the report, and check the connected data sources.

Some data sources, like Facebook ads, can be connected to Google Sheets first, and then to Looker Studio for visualization. This can be done manually or automatically using the Porter Metrics add-on.

Looker Studio Community Connectors allow direct connections to any internet-accessible data source, including commercial platforms, public datasets, and on-premise private data.

You can connect to data sources through various methods such as Web APIs, JDBC APIs, flat files (CSV, JSON, XML), and Apps Script Services.

Community Connectors can fetch data from any internet-accessible data source, making it a versatile tool for data integration.

In a real-world scenario, a Community Connector can be used to fetch data from the npm package download counts API to track download counts over time by day.

Community Connectors can be used to build a dashboard in Looker Studio to visualize data from various sources, making data analysis more accessible.

A Community Connector workflow is made up of four key functions: getAuthType(), getConfig(), getSchema(), and getData(). These functions are executed by Looker Studio at different steps in the workflow.

The Community Connector workflow has different steps, each with its own user interface and expected user actions. You can refer to the video provided for a detailed overview of the workflow.

To create a Community Connector, you'll need to define these four functions in Google Apps Script. You'll also need to switch back and forth between the Apps Script UI and the codelab.

The four functions in a Community Connector are:

These functions are used by Looker Studio at different steps in the workflow, and their responses are used in subsequent steps.

To add sources in Looker Studio, you need to sign in to your Google account credentials. You can create a new report or open an existing one to get started.

To connect a data source, you can follow six steps: login to Google Looker Studio, click on 'Add data', select a connector from the list, authorize the connection, and configure the data source. You can also add a new data source to a report by clicking on 'Resource' > 'Manage added data sources' > 'Add data source'.

Looker Studio has integrations with almost any platform and database, such as Google BigQuery, Google Sheets, Google Analytics 4, Facebook Ads, and many more. You can search for the integration you need on the search bar.

To add more than one data source to Looker Studio, you can follow the same process as adding a single data source. You can also use the deployment feature to load your connector in Looker Studio.

Here is a summary of the steps to connect a data source to Looker Studio:

Partner connectors are a game-changer for Looker Studio users. They allow you to connect your data to Looker Studio from various platforms, including marketing platforms like Facebook Ads, Hubspot, Shopify, and LinkedIn Pages.

You can find these connectors by searching for the platform's name in the Looker Studio search bar. For example, if you want to connect your Facebook Ads data, type "Facebook Ads" and select one of the available connectors.

To connect your data using a partner connector, you'll need to authorize the connection by granting necessary permissions. This may involve authenticating your account using your Facebook Profile, for instance.

Once you've authorized the connection, you'll be redirected to Looker Studio to select which account you want to connect. After that, you can add the data to your report and start creating visualizations.

Some partner connectors, like Porter Metrics, offer fully customizable templates that can save you hours of work. These templates are designed to work seamlessly with the data from the connected platform.

Here are some popular partner connectors available in Looker Studio:

Google Sheets and BigQuery are both data sources that can be used with Looker Studio. BigQuery is a cost-effective data warehouse that can handle large volumes of data, making it a good choice for datasets with more than 150,000 rows.

If you need to work with large volumes of data, it's highly recommended to connect your data to a data warehouse like Google Big Query. Each flow will represent a single table in your data warehouse, and you can then send your data to Looker Studio using its native BigQuery connection.

Google Sheets' limit is 10M cells, which is equivalent to 384,615 rows with 26 columns. If you need to work with datasets larger than this, a data warehouse like Big Query is a better option.

To connect Google Sheets to Looker Studio, you need to have every field in columns and the headers with unique names. This is the right structure to ensure a smooth connection.

To create a report in Looker Studio, you start by clicking 'add data'. Then, you select 'Google Sheets' as a data source. If you're connecting it for the first time, you'll need to authorize permissions.

When you select the spreadsheets you want to connect, make sure to choose the specific worksheet you want to visualize the data from.

Here are the steps to create a report in Looker Studio:

Select ‘Google Sheets’ as a data source. If it’s your first time connecting it you have to authorize permissions.

Select the spreadsheets you want to connect and the specific worksheet you want to visualize the data from.

You can use BigQuery with Looker Studio to bring big data into valuable insights. This is a cost-effective way to analyze large amounts of data.

BigQuery Data Transfer Service is a data import service for scheduling and moving data into BigQuery. This service helps you to easily transfer data from various sources into BigQuery.

Connecting your data to a data warehouse like Google Big Query is highly recommended when you need to work with large volumes of data. Each flow will represent a single table in your data warehouse.

For use cases with less than 150,000 rows, you can configure your Looker Studio data flow in Dataddo. However, for larger datasets, it's best to connect your data to BigQuery first.

Here are some specific use cases and recommended solutions: